Open AccessThe article concerns the adaptation of a neural tract that recognizes a helicopter from the aerodynamic and ground objects by its acoustic radiation to the helicopter flight speed. It uses non-centered informative signs-indications of estimating signal spectra, which correspond to the local extremes (maximums and minimums) of the power spectrum of input signal and have the greatest information when differentiating the helicopter signals from those of tracked vehicles. The article gives justification to the principle of the neural network (NN) adaptation and adaptation block structure, which solves problems of blade passage frequency estimation when capturing the object and track it when tracking a target, as well as forming a signal to control the resonant filter parameters of the selection block of informative signs. To create the discriminatory characteristics of the discriminator are used autoregressive statistical characteristics of the quadrature components of signal, obtained through the discrete Hilbert Converter (DGC) that perfor
Mathematical modeling of the tracking meter using the helicopter signals obtained in real conditions is performed. The article gives estimates of the tracking parameter when using a tracking meter with DGC by sequential records of realized acoustic noise of the helicopter. It also shows a block-diagram of the adaptive NN. The scientific novelty of the work is that providing the invariance of used informative sign, the counts of local extremes of power spectral density (PSD) to changes in the helicopter flight speed is reached due to adding the NN structure and adaptation block, which is implemented as a meter to track the apparent passage frequency of the helicopter rotor blades using its relationship with a function of the autoregressive acoustic signal of the helicopter.
Specialized literature proposes solutions based on the use of training classifiers with different parametric methods of spectral representations, in particular, linear prediction and cepstrum, as well as methods based on wavelet transformations and robust learning. Adaptive approach allows solving tasks in a wide range of changing helicopter speeds.